source('../settings/settings.R')
source('commonFunctions.R')
drive1 <- read.csv('../data/processed/analysis/TT1_Drive_1_PP.csv')
drive2 <- read.csv('../data/processed/Analysis/TT1_Drive_2_PP.csv')
drive3 <- read.csv('../data/processed/Analysis/TT1_Drive_3_PP.csv')
drive4 <- read.csv('../data/processed/Analysis/TT1_Drive_4_PP.csv', stringsAsFactors = T)
set.seed(43)
combinedDf <- cbind(drive4,
drive1$MeanPP_Seg0,
drive2$MeanPP, drive3$MeanPP,
drive2$StdPP, drive3$StdPP,
drive2$MeanPP_SegMax, drive3$MeanPP_SegMax,
drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
drive2$StdPP_SegMax, drive3$StdPP_SegMax,
drive2$StdPP_Seg0, drive3$StdPP_Seg0
)
names(combinedDf) <- c(names(drive4),
"PP_Dev_1_Turning",
"PP_Dev_2", "PP_Dev_3",
"Std_PP_2", "Std_PP_3",
"PP_Dev_2_Straight", "PP_Dev_3_Straight",
"PP_Dev_2_Turning", "PP_Dev_3_Turning",
"Std_PP_2_Straight", "Std_PP_3_Straight",
"Std_PP_2_Turning", "Std_PP_3_Turning"
)
combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))
# combinedDf$PP_Dev_2_Turning <- ifelse(combinedDf$PP_Dev_2_Turning > 0, combinedDf$PP_Dev_2_Turning, combinedDf$PP_Dev_2_Straight)
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]
combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE_PRIOR <- list(color='rgb(158,202,225)')
COLOR_FAILURE <- list(color='red')
yAxis <- list(
title = 'Perinasal Perspiration (Log)',
range=c(-0.3, 0.5)
)
# Apply Otsu algorithm to select threshold
ppDev <- combinedDf$PP_Dev
ppDevArray <- matrix(ppDev ,nrow = 1,ncol = length(ppDev))
THRESHOLD_MILD = otsu(ppDevArray, range=c(min(ppDev), max(ppDev))) # Expected Threshold > 0.042
print(paste0('Threshold: ', THRESHOLD_MILD))
[1] "Threshold: 0.062365390625"
MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>%
add_segments(x="#01", xend="#41", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="No Stressor")
htmltools::tagList(fig_NoStressor)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>%
add_segments(x="#02", xend="#22", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#02", xend="#22", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="Stressor = Cognitive")
htmltools::tagList(fig_Cognitive)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Arousal in Drive C - Straight segment', marker=COLOR_COGNITIVE, width=870) %>%
add_trace(y = ~PP_Dev_3_Straight, name = 'Arousal in Drive M - Straight segment', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Dev_2_Turning, name = 'Arousal in Drive C - Turning segment', marker=COLOR_COGNITIVE) %>%
add_trace(y = ~PP_Dev_3_Turning, name = 'Arousal in Drive M - Turning segment', marker=COLOR_MOTORIC) %>%
add_trace(y = ~PP_Prior, name = 'Arousal in Drive F - Under prior stressor', marker=COLOR_FAILURE_PRIOR) %>%
add_trace(y = ~PP_Dev, name = 'Arousal in Drive F - Unintended acceleration', marker=COLOR_FAILURE) %>%
add_segments(x="#05", xend="#31", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold",
line=list(color="blue", dash = 'dot')) %>%
# add_segments(x="#05", xend="#31", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
# line=list(color="darkred", dash = 'dot')) %>%
layout(yaxis = yAxis, barmode = 'group', title="Stressor = Motoric")
htmltools::tagList(fig_Motoric)
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
A marker object has been specified, but markers is not in the mode
Adding markers to the mode...
library(nlme)
combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)
combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
Linear model with all variables
combinedDfNoOutlier <- combinedDf[combinedDf$Subject != "#05",]
linearModel1 <- lm(PP_Dev ~
+ abs(PP_Dev_2_Straight)
+ abs(PP_Dev_3_Straight)
+ abs(PP_Dev_2_Turning)
+ abs(PP_Dev_3_Turning)
+ Std_PP_2_Straight
+ Std_PP_3_Straight
+ Std_PP_2_Turning
+ Std_PP_3_Turning
+ abs(PP_Prior)
+ factor(Activity),
data=combinedDf)
# anova(model)
summary(linearModel1)
Call:
lm(formula = PP_Dev ~ +abs(PP_Dev_2_Straight) + abs(PP_Dev_3_Straight) +
abs(PP_Dev_2_Turning) + abs(PP_Dev_3_Turning) + Std_PP_2_Straight +
Std_PP_3_Straight + Std_PP_2_Turning + Std_PP_3_Turning +
abs(PP_Prior) + factor(Activity), data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.054837 -0.031566 -0.006179 0.016176 0.073043
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.01505 0.06171 0.244 0.8128
abs(PP_Dev_2_Straight) -0.67715 0.35214 -1.923 0.0866 .
abs(PP_Dev_3_Straight) -0.90476 0.37179 -2.434 0.0378 *
abs(PP_Dev_2_Turning) 1.29154 0.41215 3.134 0.0121 *
abs(PP_Dev_3_Turning) 0.65586 0.40513 1.619 0.1399
Std_PP_2_Straight 0.12427 0.88972 0.140 0.8920
Std_PP_3_Straight 0.95634 0.53863 1.776 0.1095
Std_PP_2_Turning 0.02239 0.94717 0.024 0.9817
Std_PP_3_Turning -1.57139 0.74405 -2.112 0.0639 .
abs(PP_Prior) 0.59450 0.29811 1.994 0.0773 .
factor(Activity)M 0.07630 0.03754 2.033 0.0726 .
factor(Activity)NO -0.13039 0.05580 -2.337 0.0442 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.05362 on 9 degrees of freedom
Multiple R-squared: 0.8315, Adjusted R-squared: 0.6256
F-statistic: 4.039 on 11 and 9 DF, p-value: 0.02262
plot(linearModel1)




linearModel1 <- lm(PP_Dev ~
+ PP_Dev_2
+ PP_Dev_3
+ Std_PP_2
+ Std_PP_3,
data=combinedDfNoOutlier)
# anova(model)
summary(linearModel1)
Call:
lm(formula = PP_Dev ~ +PP_Dev_2 + PP_Dev_3 + Std_PP_2 + Std_PP_3,
data = combinedDfNoOutlier)
Residuals:
Min 1Q Median 3Q Max
-0.12047 -0.04606 -0.01017 0.04443 0.10684
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.13336 0.07723 -1.727 0.1048
PP_Dev_2 0.16542 0.14765 1.120 0.2802
PP_Dev_3 -0.33968 0.22814 -1.489 0.1572
Std_PP_2 0.08517 0.45813 0.186 0.8550
Std_PP_3 2.50981 1.04105 2.411 0.0292 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07381 on 15 degrees of freedom
Multiple R-squared: 0.4679, Adjusted R-squared: 0.3261
F-statistic: 3.298 on 4 and 15 DF, p-value: 0.0397
plot(linearModel1)




Linear Model from Drive C
linearModelC <- lm(PP_Dev ~
PP_Dev_2_Straight
+ PP_Dev_2_Turning
+ Std_PP_2_Straight
+ Std_PP_2_Turning,
data=combinedDf)
# anova(model)
summary(linearModelC)
Call:
lm(formula = PP_Dev ~ PP_Dev_2_Straight + PP_Dev_2_Turning +
Std_PP_2_Straight + Std_PP_2_Turning, data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.13555 -0.05433 0.00251 0.05596 0.12635
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02351 0.05388 0.436 0.668
PP_Dev_2_Straight -0.02887 0.26153 -0.110 0.913
PP_Dev_2_Turning 0.19976 0.30675 0.651 0.524
Std_PP_2_Straight 1.13112 0.84826 1.333 0.201
Std_PP_2_Turning -0.62211 0.92511 -0.672 0.511
Residual standard error: 0.08441 on 16 degrees of freedom
Multiple R-squared: 0.258, Adjusted R-squared: 0.07249
F-statistic: 1.391 on 4 and 16 DF, p-value: 0.2816
plot(linearModelC)




linearModelC_Segments <- lm(PP_Dev ~
PP_Dev_2
+ Std_PP_2,
data=combinedDf)
# anova(model)
summary(linearModelC_Segments)
Call:
lm(formula = PP_Dev ~ PP_Dev_2 + Std_PP_2, data = combinedDf)
Residuals:
Min 1Q Median 3Q Max
-0.14869 -0.05352 -0.01075 0.06704 0.13707
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02784 0.04663 0.597 0.558
PP_Dev_2 0.15451 0.11497 1.344 0.196
Std_PP_2 0.47780 0.40255 1.187 0.251
Residual standard error: 0.08518 on 18 degrees of freedom
Multiple R-squared: 0.1498, Adjusted R-squared: 0.05531
F-statistic: 1.585 on 2 and 18 DF, p-value: 0.2322
plot(linearModelC_Segments)




Linear Model from Drive M
linearModelM <- lm(PP_Dev ~
PP_Dev_3
+ Std_PP_3
+ factor(Activity),
data=combinedDfNoOutlier)
# anova(model)
summary(linearModelM)
Call:
lm(formula = PP_Dev ~ PP_Dev_3 + Std_PP_3 + factor(Activity),
data = combinedDfNoOutlier)
Residuals:
Min 1Q Median 3Q Max
-0.08243 -0.05948 -0.00697 0.05039 0.10987
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.110401 0.082369 -1.340 0.2001
PP_Dev_3 -0.070627 0.178377 -0.396 0.6977
Std_PP_3 2.020000 0.947378 2.132 0.0499 *
factor(Activity)M 0.068144 0.044012 1.548 0.1424
factor(Activity)NO -0.001664 0.038375 -0.043 0.9660
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07078 on 15 degrees of freedom
Multiple R-squared: 0.5107, Adjusted R-squared: 0.3802
F-statistic: 3.914 on 4 and 15 DF, p-value: 0.02269
plot(linearModelM)




linearModelM <- lm(PP_Dev ~
PP_Dev_3_Straight
+ PP_Dev_3_Turning
+ Std_PP_3_Straight
+ Std_PP_3_Turning,
data=combinedDfNoOutlier)
# anova(model)
summary(linearModelM)
Call:
lm(formula = PP_Dev ~ PP_Dev_3_Straight + PP_Dev_3_Turning +
Std_PP_3_Straight + Std_PP_3_Turning, data = combinedDfNoOutlier)
Residuals:
Min 1Q Median 3Q Max
-0.09868 -0.05695 -0.01233 0.06261 0.10188
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.15580 0.07868 -1.980 0.0663 .
PP_Dev_3_Straight -0.74334 0.36299 -2.048 0.0585 .
PP_Dev_3_Turning 0.64682 0.42651 1.517 0.1502
Std_PP_3_Straight 0.88491 0.69611 1.271 0.2230
Std_PP_3_Turning 1.81284 0.93698 1.935 0.0721 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.07454 on 15 degrees of freedom
Multiple R-squared: 0.4574, Adjusted R-squared: 0.3127
F-statistic: 3.162 on 4 and 15 DF, p-value: 0.04517
plot(linearModelM)




# Export the anova table
library(xtable)
lmCoeffs <- summary(linearModel1)$coefficients
lmAnova <- anova(linearModel1)
print(xtable(lmCoeffs, digits=c(0,5,5,5,5)))
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Fri Jul 10 02:45:47 2020
\begin{table}[ht]
\centering
\begin{tabular}{rrrrr}
\hline
& Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\
\hline
(Intercept) & -0.13336 & 0.07723 & -1.72671 & 0.10475 \\
PP\_Dev\_2 & 0.16542 & 0.14765 & 1.12038 & 0.28017 \\
PP\_Dev\_3 & -0.33968 & 0.22814 & -1.48892 & 0.15723 \\
Std\_PP\_2 & 0.08517 & 0.45813 & 0.18591 & 0.85500 \\
Std\_PP\_3 & 2.50981 & 1.04105 & 2.41085 & 0.02920 \\
\hline
\end{tabular}
\end{table}
print(xtable(lmAnova), digits=c(0,5,5,5,5))
% latex table generated in R 3.6.1 by xtable 1.8-4 package
% Fri Jul 10 02:45:47 2020
\begin{table}[ht]
\centering
\begin{tabular}{lrrrrr}
\hline
& Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\
\hline
PP\_Dev\_2 & 1 & 0.01 & 0.01 & 2.37 & 0.1445 \\
PP\_Dev\_3 & 1 & 0.01 & 0.01 & 1.31 & 0.2706 \\
Std\_PP\_2 & 1 & 0.02 & 0.02 & 3.70 & 0.0735 \\
Std\_PP\_3 & 1 & 0.03 & 0.03 & 5.81 & 0.0292 \\
Residuals & 15 & 0.08 & 0.01 & & \\
\hline
\end{tabular}
\end{table}
combinedDf$PP_Dev <- NULL
combinedDf$Subject <- NULL
combinedDf$Activity_NO <- ifelse(combinedDf$Activity == "NO", 1, 0)
combinedDf$Activity_C <- ifelse(combinedDf$Activity == "C", 1, 0)
combinedDf$Activity_M <- ifelse(combinedDf$Activity == "M", 1, 0)
combinedDf$Activity <- NULL
combinedDf$PP_Dev_1_Turning <- NULL
combinedDf$Std_PP_2_Straight <- NULL
combinedDf$Std_PP_2_Turning <- NULL
combinedDf$Std_PP_3_Straight <- NULL
combinedDf$Std_PP_3_Turning <- NULL
# According to Linear model
combinedDf$PP_Dev_2_Straight <- abs(combinedDf$PP_Dev_2_Straight)
combinedDf$PP_Dev_3_Straight <- abs(combinedDf$PP_Dev_3_Straight)
combinedDf$PP_Dev_2_Turning <- abs(combinedDf$PP_Dev_2_Turning)
combinedDf$PP_Dev_3_Turning <- abs(combinedDf$PP_Dev_3_Turning)
combinedDf$PP_Prior <- abs(combinedDf$PP_Prior) # NULL
combinedDf$Class <- ifelse(combinedDf$PP_Dev_Group == 1, T, F)
combinedDf$PP_Dev_Group <- NULL
print(names(combinedDf))
[1] "PP_Prior" "PP_Dev_2" "PP_Dev_3" "Std_PP_2" "Std_PP_3" "PP_Dev_2_Straight"
[7] "PP_Dev_3_Straight" "PP_Dev_2_Turning" "PP_Dev_3_Turning" "Activity_NO" "Activity_C" "Activity_M"
[13] "Class"
# library(mefa)
# combinedDf <- rep(combinedDf, 10)
set.seed(39)
n_folds <- 3
params <- param <- list(objective = "binary:logistic",
booster = "gbtree",
eval_metric = "auc",
eta = 0.1,
max_depth = 10,
alpha = 1,
lambda = 0,
gamma = 0.45,
min_child_weight = 0.3,
subsample = 1,
colsample_bytree = 1)
# XGBoost Model
xgb_m <- xgb.cv( params = param,
data = as.matrix(combinedDf %>% select(-Class)) ,
label = combinedDf$Class,
nrounds = 100,
verbose = F,
prediction = T,
maximize = T, # Change this value to F will help to run with more itineration
nfold = n_folds,
metrics = c("auc", "error"),
early_stopping_rounds = 50,
stratified = T,
scale_pos_weight = 7/14)
# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]
NA
# Prediction
combinedDf$clsPred <- round(xgb_m$pred)
computePerformanceResults <- function(sdat){
sdat = sdat[complete.cases(sdat),]
acc = sum(sdat[,1] == sdat[,2])/nrow(sdat)
conf_mat = table(sdat)
specif = conf_mat[1,1]/sum(conf_mat[,1])
sensiv = conf_mat[2,2]/sum(conf_mat[,2])
preci = conf_mat[2,2]/sum(conf_mat[2,])
npv = conf_mat[1,1]/sum(conf_mat[1,])
return(c(acc,specif,sensiv,preci,npv))
}
# Get average performance
performance <- computePerformanceResults(combinedDf %>% select(Class, clsPred))
acc <- performance[1]
prec <- performance[4]
recall <- performance[3]
spec <- performance[2]
npv <- performance[5]
f1 <- (2 * recall * prec) / (recall + prec)
auc <- as.numeric(xgb_m$evaluation_log[xgb_m$best_iteration, "test_auc_mean"])
print(paste("Accuracy=", round(acc, 2)))
[1] "Accuracy= 0.9"
print(paste("Precision=", round(prec, 2)))
[1] "Precision= 0.92"
print(paste("Recall=", round(recall, 2)))
[1] "Recall= 0.92"
print(paste("Specificity=", round(spec, 2)))
[1] "Specificity= 0.88"
print(paste("NPV=", round(npv, 2)))
[1] "NPV= 0.88"
print(paste("F1=", round(f1, 2)))
[1] "F1= 0.92"
print(paste("AUC=", round(auc, 2)))
[1] "AUC= 0.91"
# Importance
bst <- xgboost( params = param,
data = as.matrix(combinedDf %>% select(-Class)) ,
label = combinedDf$Class,
nrounds = 100,
verbose = F,
prediction = T,
maximize = T, # Change this value to F will help to run with more itineration
nfold = n_folds,
metrics = c("auc", "error"),
early_stopping_rounds = 50,
stratified = T,
scale_pos_weight = 1)
importanceDf <- xgb.importance(colnames(combinedDf), model = bst)
print(importanceDf)
library(pROC)
dfROC <- pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
predictor = round(xgb_m$pred),
levels=c(0, 1), direction = "<")
# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter
plot(pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
predictor = round(xgb_m$pred),
levels=c(0, 1), direction = "<"),
legacy.axes = TRUE,
main="ROC Curve",
lwd=1.5)

Important features
# Eleminate #5 who has an exceptional data to find a better threshold
stdPP3 <- sort(importantFeaturesDf$Std_PP_3, decreasing = T)[2:length(importantFeaturesDf$Std_PP_3)]
stdPP3Array <- matrix(stdPP3 ,nrow = 1,ncol = length(stdPP3))
maxStdPP3 <- sort(importantFeaturesDf$Std_PP_3, decreasing = T)[2]
PP_DEV_3_THRESHOLD <- otsu(stdPP3Array, range=c(min(stdPP3), maxStdPP3)) # Expected Threshold = 0.088
print(paste0('Threshold: ', PP_DEV_3_THRESHOLD))
[1] "Threshold: 0.0881111526518663"
importantFeaturesDf$PP_Dev_3_Group <- ifelse(importantFeaturesDf$Std_PP_3 > PP_DEV_3_THRESHOLD, 1, 0)
write.csv(importantFeaturesDf, "../outputs/importantFeatures.csv")
Venn diagram
library(VennDiagram)
library(RColorBrewer)
M_Low <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_3_Group==0,])
M_High <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_3_Group==1,])
F_Low <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_Group==0,])
F_High <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_Group==1,])
jpeg("../plots/venn/venn_All.png", res=150, width=900)
venn.plot <- venn.diagram(
list(M_Low, F_Low, M_High, F_High), NULL,
fill=c("blue", "blue", "red", "red"),
alpha=c(0.5,0.5,0.5,0.5),
resolution = 150,
cex = 1,
cat.fontface=1,
category.names=c("Drive=M\n SD=Low\n", "Drive=F\n Arousal=Low\n", "Drive=M\n SD=High\n", "Drive=F\n Arousal=High\n")
)
grid.draw(venn.plot)
dev.off()
null device
1
#
# jpeg("../plots/venn/venn_High.png", res=150, width=700)
# venn.plot <- venn.diagram(
# list(M_High, F_High), NULL,
# fill=c("pink", "red"),
# alpha=c(0.5,0.5),
# resolution = 150,
# cex = 1,
# cat.fontface=1,
# category.names=c("Drive=M", "Drive=F")
# )
# grid.draw(venn.plot)
# dev.off()
Plot feature importance
yAxis <- list(
title = 'Importance',
range=c(0.0, 1.0)
)
xAxis <- list(
title = ''
)
importanceDf$FeatureName <- lapply(importanceDf$Feature, function(x) {
ifelse(x=="Std_PP_3", "SD of Arousal\n in Drive M",
ifelse(x=="PP_Dev_2_Turning", "Arousal in Drive C\nat turning segments",
ifelse(x=="Activity_C", "Prior stressor\n is Cognitive", x)))
})
fig_Importance <- plot_ly(importanceDf, x = ~FeatureName, y = ~Gain, type = 'bar', name = 'Gain', width=600) %>%
add_trace(y = ~Cover, name = 'Cover') %>%
add_trace(y = ~Frequency, name = 'Frequency') %>%
layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', title="Feature Importance") %>%
config(.Last.value, mathjax = 'cdn')
htmltools::tagList(fig_Importance)
Feature
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, x = ~Std_PP_3, y = ~PP_Dev,
color=~factor(PP_Dev_Group), colors=classColors,
marker=list(text="X")) %>%
layout(xaxis=list(title="SD of Arousal in Motoric Drive \n Hyphenated line indicates discriminative boundary"), yaxis=list(title="Arousal at catastrophic event"), showscale=F) %>%
layout(shapes=list(
list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1, color="green"))
))
htmltools::tagList(figStdVsDev)
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
'layout' objects don't have these attributes: 'showscale'
Valid attributes include:
'font', 'title', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
'layout' objects don't have these attributes: 'showscale'
Valid attributes include:
'font', 'title', 'autosize', 'width', 'height', 'margin', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, x = ~abs(PP_Dev_2_Turning), y = ~PP_Dev,
color=~factor(PP_Dev_Group), colors=classColors,
marker=list(text="X")) %>%
layout(xaxis=list(title="SD of Arousal in Motoric Drive"), yaxis=list(title="Arousal at catastrophic event")) %>%
layout(shapes=list(
list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1))
))
htmltools::tagList(figStdVsDev)
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, y = ~abs(PP_Dev_2_Turning), x = ~Std_PP_3,
color=~factor(PP_Dev_Group), colors=classColors,
marker=list(text="X")) %>%
layout(xaxis=list(title="SD of Arousal in Motoric Drive"), yaxis=list(title="Arousal at catastrophic event"), showscale=F) %>%
layout(shapes=list(
list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1))
))
htmltools::tagList(figStdVsDev)
---
title: "R Notebook"
output: html_notebook
---

```{r}
source('../settings/settings.R')
source('commonFunctions.R')
```

```{r}
drive1 <- read.csv('../data/processed/analysis/TT1_Drive_1_PP.csv')
drive2 <- read.csv('../data/processed/Analysis/TT1_Drive_2_PP.csv')
drive3 <- read.csv('../data/processed/Analysis/TT1_Drive_3_PP.csv')
drive4 <- read.csv('../data/processed/Analysis/TT1_Drive_4_PP.csv', stringsAsFactors = T)
```

```{r}
set.seed(43)
combinedDf <- cbind(drive4, 
                    drive1$MeanPP_Seg0, 
                    drive2$MeanPP, drive3$MeanPP,
                    drive2$StdPP, drive3$StdPP,
                    drive2$MeanPP_SegMax, drive3$MeanPP_SegMax, 
                    drive2$MeanPP_Seg0, drive3$MeanPP_Seg0,
                    drive2$StdPP_SegMax, drive3$StdPP_SegMax, 
                    drive2$StdPP_Seg0, drive3$StdPP_Seg0
                  )
names(combinedDf) <- c(names(drive4), 
                       "PP_Dev_1_Turning",
                       "PP_Dev_2", "PP_Dev_3", 
                       "Std_PP_2", "Std_PP_3",
                       "PP_Dev_2_Straight", "PP_Dev_3_Straight", 
                       "PP_Dev_2_Turning", "PP_Dev_3_Turning", 
                       "Std_PP_2_Straight", "Std_PP_3_Straight", 
                       "Std_PP_2_Turning", "Std_PP_3_Turning"
                       )

combinedDf$Subject <- paste0("#", str_pad(combinedDf$Subject, 2, pad="0"))

# combinedDf$PP_Dev_2_Turning <- ifelse(combinedDf$PP_Dev_2_Turning > 0, combinedDf$PP_Dev_2_Turning, combinedDf$PP_Dev_2_Straight)
```

```{r}
combinedDf_NoStressor <- combinedDf[combinedDf$Activity == "NO",]
combinedDf_Cognitive <- combinedDf[combinedDf$Activity == "C",]
combinedDf_Motoric <- combinedDf[combinedDf$Activity == "M",]

combinedDf_NoStressor$Subject <- as.factor(combinedDf_NoStressor$Subject)
combinedDf_Cognitive$Subject <- as.factor(combinedDf_Cognitive$Subject)
combinedDf_Motoric$Subject <- as.factor(combinedDf_Motoric$Subject)
```

```{r}
COLOR_NORMAL <- list(color='rgb(120,120,120)')
COLOR_COGNITIVE <- list(color='rgb(158,202,225)')
COLOR_MOTORIC <- list(color='rgb(58,200,225)')
COLOR_FAILURE_PRIOR <- list(color='rgb(158,202,225)')
COLOR_FAILURE <- list(color='red')

yAxis <- list(
  title = 'Perinasal Perspiration (Log)',
  range=c(-0.3, 0.5)
)

# Apply Otsu algorithm to select threshold
ppDev <- combinedDf$PP_Dev
ppDevArray <- matrix(ppDev ,nrow = 1,ncol = length(ppDev))
  
THRESHOLD_MILD = otsu(ppDevArray, range=c(min(ppDev), max(ppDev))) # Expected Threshold > 0.042
print(paste0('Threshold: ', THRESHOLD_MILD))

MARKER_LINE_MILD = list(color="blue")
MARKER_LINE_EXTREME = list(color="red")
```

```{r, warning=F}
fig_NoStressor <- plot_ly(combinedDf_NoStressor, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>% 
  add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>% 
  add_segments(x="#01", xend="#41", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#01", xend="#41", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="No Stressor")

htmltools::tagList(fig_NoStressor)
```

```{r, warning=F}
fig_Cognitive <- plot_ly(combinedDf_Cognitive, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Cognitive - Mean PP (Straight)', marker=COLOR_COGNITIVE) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Motoric - Mean PP (Straight)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Cognitive - Mean PP (Turning)', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Motoric - Mean PP (Turning)', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Prior, name = 'Failure - Prior PP', marker=COLOR_FAILURE_PRIOR) %>% 
  add_trace(y = ~PP_Dev, name = 'Failure - PP Deviation', marker=COLOR_FAILURE) %>% 
  add_segments(x="#02", xend="#22", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold: Mild Change of PP",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#02", xend="#22", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="Stressor = Cognitive")

htmltools::tagList(fig_Cognitive)
```



```{r, warning=F}
fig_Motoric <- plot_ly(combinedDf_Motoric, x = ~Subject, y = ~PP_Dev_2_Straight, type = 'bar', name = 'Arousal in Drive C - Straight segment', marker=COLOR_COGNITIVE, width=870) %>%
  add_trace(y = ~PP_Dev_3_Straight, name = 'Arousal in Drive M - Straight segment', marker=COLOR_MOTORIC) %>% 
  add_trace(y = ~PP_Dev_2_Turning, name = 'Arousal in Drive C - Turning segment', marker=COLOR_COGNITIVE) %>% 
  add_trace(y = ~PP_Dev_3_Turning, name = 'Arousal in Drive M - Turning segment', marker=COLOR_MOTORIC) %>%
  add_trace(y = ~PP_Prior, name = 'Arousal in Drive F - Under prior stressor', marker=COLOR_FAILURE_PRIOR) %>% 
  add_trace(y = ~PP_Dev, name = 'Arousal in Drive F - Unintended acceleration', marker=COLOR_FAILURE) %>% 
  add_segments(x="#05", xend="#31", y = THRESHOLD_MILD, yend = THRESHOLD_MILD, name="Threshold",
                           line=list(color="blue", dash = 'dot')) %>%
  # add_segments(x="#05", xend="#31", y = THRESHOLD_EXTREME, yend = THRESHOLD_EXTREME, name="Threshold: Extreme Change of PP",
  #                          line=list(color="darkred", dash = 'dot')) %>%
  layout(yaxis = yAxis, barmode = 'group', title="Stressor = Motoric")

htmltools::tagList(fig_Motoric)
```


```{r}
library(nlme)
combinedDf$Subject = as.factor(combinedDf$Subject)
combinedDf$Activity = as.factor(combinedDf$Activity)
combinedDf$PP_Dev_Group = ifelse(combinedDf$PP_Dev > THRESHOLD_MILD, 1, 0)
```

### Extract data for important features
```{r}
importantFeaturesDf <- combinedDf %>% select(Subject, Std_PP_3, PP_Dev_2_Turning, Activity, PP_Dev, PP_Dev_Group)
```

# Linear model with all variables
```{r}
combinedDfNoOutlier <- combinedDf[combinedDf$Subject != "#05",]
linearModel1 <- lm(PP_Dev ~ 
              + abs(PP_Dev_2_Straight)
              + abs(PP_Dev_3_Straight)
              + abs(PP_Dev_2_Turning) 
              + abs(PP_Dev_3_Turning)
              + Std_PP_2_Straight
              + Std_PP_3_Straight
              + Std_PP_2_Turning
              + Std_PP_3_Turning
              + abs(PP_Prior)
              + factor(Activity), 
            data=combinedDf)

# anova(model)
summary(linearModel1)
plot(linearModel1)
```

```{r}
linearModel1 <- lm(PP_Dev ~ 
              + PP_Dev_2
              + PP_Dev_3
              + Std_PP_2
              + Std_PP_3, 
            data=combinedDfNoOutlier)

# anova(model)
summary(linearModel1)
plot(linearModel1)
```


# Linear Model from Drive C
```{r}
linearModelC <- lm(PP_Dev ~
              PP_Dev_2_Straight
              + PP_Dev_2_Turning
              + Std_PP_2_Straight
              + Std_PP_2_Turning,
            data=combinedDf)

# anova(model)
summary(linearModelC)
plot(linearModelC)
```

```{r}
linearModelC_Segments <- lm(PP_Dev ~ 
              PP_Dev_2
              + Std_PP_2,
            data=combinedDf)

# anova(model)
summary(linearModelC_Segments)
plot(linearModelC_Segments)
```

# Linear Model from Drive M
```{r}
linearModelM <- lm(PP_Dev ~ 
              PP_Dev_3
              + Std_PP_3
              + factor(Activity),
            data=combinedDfNoOutlier)

# anova(model)
summary(linearModelM)
plot(linearModelM)
```
```{r}
linearModelM <- lm(PP_Dev ~ 
              PP_Dev_3_Straight
              + PP_Dev_3_Turning
              + Std_PP_3_Straight
              + Std_PP_3_Turning,
            data=combinedDfNoOutlier)

# anova(model)
summary(linearModelM)
plot(linearModelM)
```

```{r}
# Export the anova table
library(xtable)
lmCoeffs <- summary(linearModel1)$coefficients
lmAnova <- anova(linearModel1)

print(xtable(lmCoeffs, digits=c(0,5,5,5,5)))
print(xtable(lmAnova), digits=c(0,5,5,5,5))

```


```{r}
combinedDf$PP_Dev <- NULL

combinedDf$Subject <- NULL
combinedDf$Activity_NO <- ifelse(combinedDf$Activity == "NO", 1, 0)
combinedDf$Activity_C <- ifelse(combinedDf$Activity == "C", 1, 0)
combinedDf$Activity_M <- ifelse(combinedDf$Activity == "M", 1, 0)
combinedDf$Activity <- NULL
combinedDf$PP_Dev_1_Turning <- NULL

combinedDf$Std_PP_2_Straight <- NULL
combinedDf$Std_PP_2_Turning <- NULL
combinedDf$Std_PP_3_Straight <- NULL
combinedDf$Std_PP_3_Turning <- NULL

# According to Linear model
combinedDf$PP_Dev_2_Straight <- abs(combinedDf$PP_Dev_2_Straight)
combinedDf$PP_Dev_3_Straight <- abs(combinedDf$PP_Dev_3_Straight)
combinedDf$PP_Dev_2_Turning <- abs(combinedDf$PP_Dev_2_Turning)
combinedDf$PP_Dev_3_Turning <- abs(combinedDf$PP_Dev_3_Turning)
combinedDf$PP_Prior <- abs(combinedDf$PP_Prior) # NULL

combinedDf$Class <- ifelse(combinedDf$PP_Dev_Group == 1, T, F)
combinedDf$PP_Dev_Group <- NULL

print(names(combinedDf))
```

```{r}
# library(mefa)
# combinedDf <- rep(combinedDf, 10) 
```

```{r}
set.seed(39)
n_folds <- 3
params <- param <- list(objective       = "binary:logistic", 
               booster          = "gbtree",
               eval_metric      = "auc",
               eta              = 0.1,
               max_depth        = 10,
               alpha            = 1,
               lambda           = 0,
               gamma            = 0.45,
               min_child_weight = 0.3,
               subsample        = 1,
               colsample_bytree = 1)
           
# XGBoost Model         
xgb_m <- xgb.cv(   params               = param,
                  data = as.matrix(combinedDf %>% select(-Class)) ,
                  label =  combinedDf$Class,
                  nrounds             = 100,
                  verbose             = F,
                  prediction          = T,
                  maximize            = T, # Change this value to F will help to run with more itineration
                  nfold               = n_folds,
                  metrics             = c("auc", "error"),
                  early_stopping_rounds = 50,
                  stratified            = T,
                  scale_pos_weight      = 7/14)

# xgb_m$evaluation_log[xgb_m$best_iteration,"test_auc_mean"]
xgb_m$evaluation_log[xgb_m$best_iteration,]

```
```{r}
# Prediction
combinedDf$clsPred <- round(xgb_m$pred)

computePerformanceResults <- function(sdat){
  sdat = sdat[complete.cases(sdat),]
  acc = sum(sdat[,1] == sdat[,2])/nrow(sdat)
  conf_mat = table(sdat)
  specif = conf_mat[1,1]/sum(conf_mat[,1])
  sensiv = conf_mat[2,2]/sum(conf_mat[,2])
  preci =  conf_mat[2,2]/sum(conf_mat[2,])
  npv =    conf_mat[1,1]/sum(conf_mat[1,])
  return(c(acc,specif,sensiv,preci,npv))
}

# Get average performance
performance <- computePerformanceResults(combinedDf %>% select(Class, clsPred))
acc <- performance[1]
prec <- performance[4]
recall <- performance[3]
spec <- performance[2]
npv <- performance[5]
f1 <- (2 * recall * prec) / (recall + prec)
auc <- as.numeric(xgb_m$evaluation_log[xgb_m$best_iteration, "test_auc_mean"])

print(paste("Accuracy=", round(acc, 2)))
print(paste("Precision=", round(prec, 2)))
print(paste("Recall=", round(recall, 2)))
print(paste("Specificity=", round(spec, 2)))
print(paste("NPV=", round(npv, 2)))
print(paste("F1=", round(f1, 2)))
print(paste("AUC=", round(auc, 2)))
```

```{r}
# Importance
bst <- xgboost(   params               = param,
                  data = as.matrix(combinedDf %>% select(-Class)) ,
                  label =  combinedDf$Class,
                  nrounds             = 100,
                  verbose             = F,
                  prediction          = T,
                  maximize            = T, # Change this value to F will help to run with more itineration
                  nfold               = n_folds,
                  metrics             = c("auc", "error"),
                  early_stopping_rounds = 50,
                  stratified            = T,
                  scale_pos_weight      = 1)
importanceDf <- xgb.importance(colnames(combinedDf), model = bst)
print(importanceDf)
```

```{r}
library(pROC)

dfROC <- pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
               predictor = round(xgb_m$pred),
               levels=c(0, 1), direction = "<")

# it = which.max(xgb_m$evaluation_log$test_auc_mean)
# best.iter = xgb_m$evaluation_log$iter[it]
# best.iter 

plot(pROC::roc(response = ifelse(combinedDf$Class==T, 1, 0),
               predictor = round(xgb_m$pred),
               levels=c(0, 1), direction = "<"), 
     legacy.axes = TRUE,
     main="ROC Curve", 
     lwd=1.5) 
```

# Important features
```{r}
# Eleminate #5 who has an exceptional data to find a better threshold
stdPP3 <- sort(importantFeaturesDf$Std_PP_3, decreasing = T)[2:length(importantFeaturesDf$Std_PP_3)]
stdPP3Array <- matrix(stdPP3 ,nrow = 1,ncol = length(stdPP3))
  
maxStdPP3 <- sort(importantFeaturesDf$Std_PP_3, decreasing = T)[2]
PP_DEV_3_THRESHOLD <- otsu(stdPP3Array, range=c(min(stdPP3), maxStdPP3)) # Expected Threshold = 0.088
print(paste0('Threshold: ', PP_DEV_3_THRESHOLD))

importantFeaturesDf$PP_Dev_3_Group <- ifelse(importantFeaturesDf$Std_PP_3 > PP_DEV_3_THRESHOLD, 1, 0)
write.csv(importantFeaturesDf, "../outputs/importantFeatures.csv")
```

# Venn diagram
```{r}
library(VennDiagram)
library(RColorBrewer)
 
M_Low <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_3_Group==0,])
M_High <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_3_Group==1,])

F_Low <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_Group==0,])
F_High <- rownames(importantFeaturesDf[importantFeaturesDf$PP_Dev_Group==1,])

jpeg("../plots/venn/venn_All.png", res=150, width=900)
venn.plot <- venn.diagram(
  list(M_Low, F_Low, M_High, F_High), NULL, 
  fill=c("blue", "blue", "red", "red"), 
  alpha=c(0.5,0.5,0.5,0.5), 
  resolution = 150,
  cex = 1, 
  cat.fontface=1, 
  category.names=c("Drive=M\n SD=Low\n", "Drive=F\n Arousal=Low\n", "Drive=M\n SD=High\n", "Drive=F\n Arousal=High\n")
)
grid.draw(venn.plot)
dev.off()
# 
# jpeg("../plots/venn/venn_High.png", res=150, width=700)
# venn.plot <- venn.diagram(
#   list(M_High, F_High), NULL, 
#   fill=c("pink", "red"), 
#   alpha=c(0.5,0.5), 
#   resolution = 150,
#   cex = 1, 
#   cat.fontface=1, 
#   category.names=c("Drive=M", "Drive=F")
# )
# grid.draw(venn.plot)
# dev.off()
```

### Plot feature importance
```{r}
yAxis <- list(
  title = 'Importance',
  range=c(0.0, 1.0)
)
xAxis <- list(
  title = ''
)
importanceDf$FeatureName <- lapply(importanceDf$Feature, function(x) {
  ifelse(x=="Std_PP_3", "SD of Arousal\n in Drive M", 
         ifelse(x=="PP_Dev_2_Turning", "Arousal in Drive C\nat turning segments", 
            ifelse(x=="Activity_C", "Prior stressor\n is Cognitive", x)))
})

fig_Importance <- plot_ly(importanceDf, x = ~FeatureName, y = ~Gain, type = 'bar', name = 'Gain', width=600) %>%
  add_trace(y = ~Cover, name = 'Cover') %>% 
  add_trace(y = ~Frequency, name = 'Frequency') %>% 
  layout(yaxis = yAxis, xaxis=xAxis, barmode = 'group', title="Feature Importance") %>% 
  config(.Last.value, mathjax = 'cdn')

htmltools::tagList(fig_Importance)
```

### Feature
```{r}
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, x = ~Std_PP_3, y = ~PP_Dev, 
                       color=~factor(PP_Dev_Group), colors=classColors,
                       marker=list(text="X")) %>%
  layout(xaxis=list(title="SD of Arousal in Motoric Drive \n Hyphenated line indicates discriminative boundary"), yaxis=list(title="Arousal at catastrophic event"), showscale=F) %>%
  layout(shapes=list(
    list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1, color="green"))
  ))
htmltools::tagList(figStdVsDev)
```

```{r}
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, x = ~abs(PP_Dev_2_Turning), y = ~PP_Dev, 
                       color=~factor(PP_Dev_Group), colors=classColors,
                       marker=list(text="X")) %>%
  layout(xaxis=list(title="SD of Arousal in Motoric Drive"), yaxis=list(title="Arousal at catastrophic event")) %>%
  layout(shapes=list(
    list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1))
  ))
htmltools::tagList(figStdVsDev)
```

```{r}
classColors <- c("blue", "red")
figStdVsDev <- plot_ly(data = importantFeaturesDf, y = ~abs(PP_Dev_2_Turning), x = ~Std_PP_3, 
                       color=~factor(PP_Dev_Group), colors=classColors,
                       marker=list(text="X")) %>%
  layout(xaxis=list(title="SD of Arousal in Motoric Drive"), yaxis=list(title="Arousal at catastrophic event"), showscale=F) %>%
  layout(shapes=list(
    list(x0=0.088, x1=0.088, y0=-0.1, y1=0.25, line=list(dash="dot", width=1))
  ))
htmltools::tagList(figStdVsDev)
```


